1. Field of the Invention
The invention relates to the field of video surveillance systems.
2. Description of the Prior Art
Around the world as the awareness of both crime and technology become more prevalent, officials find themselves relying more and more on video surveillance as a cure-all in the name of public safety. Used properly, video cameras help expose wrongdoing, but typically come at the cost of privacy to those not involved in any maleficent activity.
With the heightened consciousness among the public, private and government organizations for security, surveillance technologies, especially video surveillance, have received a lot of public attention. Video surveillance systems are being considered or deployed in a variety of public spaces such as metro stations, airports, shipping docks, etc. As cameras are installed in more places, so do legitimate concerns about invasion of privacy. Privacy advocates worry whether the potential abuses of video surveillance might ultimately outweigh its benefits.
Recently, there has been a increased interest in RFID-related research, both in the academic and commercial sectors. Particularly, solutions examining the threat to consumer privacy of RFID technology have proposed techniques to protect unwanted scanning of RFID tags attached to items consumers may be carrying or wearing. For example, some researchers propose the use of ‘selecting blocking’ by ‘blocker tags’ to protect consumer privacy threatened by the pervasive use of RFID tags on consumer products. This enables consumers to ‘hide’ or ‘reveal’ certain RFID tags from scanning when they want to.
Therefore, this type of art addresses security concerns relating only to the RFID hardware itself. Privacy concerns in video surveillance have not really been addressed in video processing research. Furthermore, these techniques require efficient implementations to process real-time video streams (usually MPEG-1 or MPEG-2). Variations of background subtraction has been used as a technique for foreground/background segmentation for long video sequences. As a relatively simple method, it works fairly well in most cases but its performance depends heavily on the accuracy of the background estimation algorithms.
A quasi-automatic video surveillance approach has been proposed by Marchesotti, L. et. al., “A Video Surveillance Architecture for Alarm Generation and Video Sequences Retrieval” in ICIP2002 (2002) based on event triggers to generate alarms and overcome the drawbacks of traditional systems.
An approach known as experimental sampling was proposed in Wang, J. et. al., “Experiential Sampling for video surveillance in the First ACM SIGMM international workshop on Video surveillance (2003), ACM Press, pp. 77-86, which carries out analysis on the environment and selects data of interest while discarding the irrelevant data.
The area of work dealing with privacy preservation in media spaces is relatively new, and a lot of the related work is in the domain of computer-supported corporative work (CSCW) Dourish, P et. al. “Awareness and Coordination in Shared Workspaces” in CSCW'92, Toronto (1992), ACM Press, New York, N.Y., pp. 107-114. and Zhao, Q. et. al., “Evaluating Image Filtering Based Techniques in Media Space Applications” in CSCW'98, Seattle) (1998), ACM Press, New York, N.Y., pp. 11-18. Of particular interest is the work presented by Boyle, M. et. al., “The Effects of Filtered Video on Awareness and Privacy” in Proc. of CSCW'00 (2000), pp. 1-10, which utilized blur and pixelization filters to mask sensitive details in video while still providing a low-fidelity overview useful for awareness. Specifically they analyze how blur and pixelize video filters impact both awareness and privacy in a media space. However, the limitation of these techniques are that the filters are not applied to the individual objects in the video but to the entire video frame, which makes enforcing separate policies and distinguishing between authorized and unauthorized personnel impossible.
Previous work utilizing eigenspace filters in Crowley, J. et. al. “Things That See” in Communications of the ACM, 43: 3 (March) (2000), ACM Press, New York, N.Y., pp. 54-64, proposed a way to mask out potentially sensitive action associated with an individual by using a set of pre-defined base images to extract a representation of the person (face) by taking an inner product of the video images with those base images stored in a database. This technique though useful, relies on capturing and storing base images of the potential subjects, which may be both infeasible as well as against the notion of trying to store as little identifiable information about individuals in the space as possible. There has been a large body of work on policy specification and access control in XML Bertino, E. et. al., “Controlled Access and Dissemination of XML Documents” in WIDM'99 ACM (1999) and Damiani, E., and di Vimercati et. al, “A Fine-Grained Access Control System for XML Documents” in ACM Transactions on Information and System Security (TISSEC) (2002), vol. 5, num 2, pp. 169-200. The majority provide run-time checking of access control policies and fine-grained specification of policy.
IBM Smart Video Surveillance Project is a project which focuses on privacy protection in physical spaces. However, the architecture of the invention is more general in that video is simply one example of a sensor that can be used to collect data within pervasive spaces. Additionally, we have specified a policy language for expressing access control constraints and providing group-based (inventory-person combinations) security, while in the IBM project each access policy is based on the identity of the user. Hence the system needs to identify a user to enforce the policy which seems to violate the privacy of the users.
Additionally, it is not clear how localization may be achieved in this system. Solutions like face recognition are proposed but this also suffers from the fact that identification of the users is necessary at some level. Our system abstracts the identity of the users from the access control policies and utilizes additional sensors for localization, all of which can be achieved in a manner in which privacy is protected.
Current data collection technologies usually neglect issues of privacy in carrying out their respective functions. An example of this is modern video surveillance, which we see in our daily lives and is usually accepted as being intrusive with no provisions for the preservation of privacy of the monitored subjects.
What is needed is a framework for privacy-protecting data collection in media spaces.
What is needed is some kind of intelligent surveillance system that is more selective in what video is captured, and which focuses on anomalous events while protecting the privacy of authorized personnel.